meta-iNat and tiered meta-iNat: Our implementation follows [1]}, only we train for twice as long, as we found that the loss curves frequently failed to stabilize before each decrease in learning rate. We therefore train our models for 100 epochs using Adam with initial learning rate 1e-3. We cut the learning rate by a factor of two every 20 episodes. Because there is no validation set available, we simply use the final model at the end of training. Episode setup is as in CUB and Aircraft.
